Inspired by [1]}, we split the training procedure into stages and enable the model to progressively achieve fine-detailed restoration.
To further elaborate, we use \(T_i^s\)  to denote a single training step in \(s\) -th stage to restore \(\mathbf {I}_{i}\) . For the first stage, \(T^1_i\)  takes \(\mathbf {I}_{i-1}\) , \(\mathbf {I}_{i}\)  and \(\mathbf {I}_{i+1}\)  and outputs \(\mathbf {R}_{i}^1\)  as a restoration for \(\mathbf {I}_{i}\) .
The later stages take the output of the previous stage as input.
For example, \(T_i^2\)  takes \(\mathbf {R}_{i-1}^1\) , \(\mathbf {R}_{i}^1\)  and \(\mathbf {R}_{i+1}^1\)  in order to restore \(\mathbf {I}_{i}\) .
In this way, a two-staged learning requires five consecutive frames as input.
